Currently the most popular method of providing robustness certificates is randomized smoothing where an input is smoothed via some probability distribution. We propose a novel approach to randomized smoothing over multiplicative parameters. Using this method we construct certifiably robust classifiers with respect to a gamma correction perturbation and compare the result with classifiers obtained via other smoothing distributions (Gaussian, Laplace, uniform). The experiments show that asymmetrical Rayleigh distribution allows to obtain better certificates for some values of perturbation parameters. To the best of our knowledge it is the first work concerning certified robustness against the multiplicative gamma correction transformation and the first to study effects of asymmetrical distributions in randomized smoothing.
翻译:目前,提供稳健度证书的最常用方法是随机滑动,输入通过某种概率分布得到平稳。我们提出了一种新颖的随机滑动方法,在多复制参数上进行随机滑动。我们使用这种方法在伽玛校正扰动方面建立可验证的稳健分类器,并将结果与其他平滑分布(Gausian、Laplace、制服)获得的分类器进行比较。实验表明,对称雷利的分布使得某些扰动参数的数值能够获得更好的证书。 据我们所知,这是针对多复制伽马校正转换的经认证稳健度的首次工作,也是研究随机平滑过程中对称分布效应的第一次工作。